AutoML Leaderboard
| Best model | name | model_type | metric_type | metric_value | train_time | single_prediction_time |
|---|---|---|---|---|---|---|
| 1_Default_LightGBM | LightGBM | rmse | 170.896 | 5.31 | 0.0089 | |
| 2_Default_Xgboost | Xgboost | rmse | 170.468 | 2.01 | 0.0101 | |
| 3_Default_CatBoost | CatBoost | rmse | 169.979 | 1.97 | 0.0088 | |
| 4_Default_NeuralNetwork | Neural Network | rmse | 170.801 | 4.68 | 0.0146 | |
| 5_Default_RandomForest | Random Forest | rmse | 172.298 | 5.72 | 0.0874 | |
| 10_LightGBM | LightGBM | rmse | 172.388 | 3.95 | 0.0089 | |
| 6_Xgboost | Xgboost | rmse | 171.529 | 2.09 | 0.0105 | |
| 14_CatBoost | CatBoost | rmse | 169.973 | 542.65 | 0.0084 | |
| 15_CatBoost | CatBoost | rmse | 170.015 | 9.68 | 0.0084 | |
| 16_CatBoost | CatBoost | rmse | 169.981 | 2.76 | 0.0083 | |
| 17_CatBoost | CatBoost | rmse | 170.025 | 3.35 | 0.0083 | |
| 18_Xgboost | Xgboost | rmse | 170.467 | 2.33 | 0.0104 | |
| 19_Xgboost | Xgboost | rmse | 170.445 | 1.86 | 0.0102 | |
| 20_NeuralNetwork | Neural Network | rmse | 170.405 | 4.28 | 0.0149 | |
| 21_NeuralNetwork | Neural Network | rmse | 171.245 | 4.04 | 0.0145 | |
| the best | Ensemble | Ensemble | rmse | 169.89 | 0.2 | 0.0728 |
AutoML Performance
AutoML Performance Boxplot
Features Importance (Original Scale)
Scaled Features Importance (MinMax per Model)
Spearman Correlation of Models
Summary of 4_Default_NeuralNetwork
Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 16
- learning_rate: 0.05
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
4.5 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.784 |
| MSE | 29172.8 |
| RMSE | 170.801 |
| R2 | 0.620462 |
| MAPE | 0.19442 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 18_Xgboost
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: reg:squarederror
- eta: 0.05
- max_depth: 6
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
2.1 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.488 |
| MSE | 29059.1 |
| RMSE | 170.467 |
| R2 | 0.621941 |
| MAPE | 0.194198 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 17_CatBoost
CatBoost
- n_jobs: -1
- learning_rate: 0.1
- depth: 6
- rsm: 0.9
- loss_function: RMSE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
3.1 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.061 |
| MSE | 28908.3 |
| RMSE | 170.025 |
| R2 | 0.623903 |
| MAPE | 0.193339 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 16_CatBoost
CatBoost
- n_jobs: -1
- learning_rate: 0.05
- depth: 8
- rsm: 0.9
- loss_function: RMSE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
2.5 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.06 |
| MSE | 28893.4 |
| RMSE | 169.981 |
| R2 | 0.624097 |
| MAPE | 0.193587 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of Ensemble
Ensemble structure
| Model | Weight |
|---|---|
| 14_CatBoost | 2 |
| 16_CatBoost | 1 |
| 17_CatBoost | 1 |
| 19_Xgboost | 2 |
| 20_NeuralNetwork | 2 |
| 3_Default_CatBoost | 4 |
| 4_Default_NeuralNetwork | 1 |
Metric details:
| Metric | Score |
|---|---|
| MAE | 131.979 |
| MSE | 28862.7 |
| RMSE | 169.89 |
| R2 | 0.624496 |
| MAPE | 0.19343 |
Learning curves
True vs Predicted
Predicted vs Residuals
Summary of 19_Xgboost
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: reg:squarederror
- eta: 0.1
- max_depth: 6
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
1.6 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.481 |
| MSE | 29051.4 |
| RMSE | 170.445 |
| R2 | 0.622041 |
| MAPE | 0.19421 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 21_NeuralNetwork
Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 16
- learning_rate: 0.08
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
3.8 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 133.129 |
| MSE | 29324.9 |
| RMSE | 171.245 |
| R2 | 0.618484 |
| MAPE | 0.194396 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 10_LightGBM
LightGBM
- n_jobs: -1
- objective: regression
- num_leaves: 15
- learning_rate: 0.05
- feature_fraction: 0.8
- bagging_fraction: 0.5
- min_data_in_leaf: 50
- metric: rmse
- custom_eval_metric_name: None
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
3.7 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 134.194 |
| MSE | 29717.5 |
| RMSE | 172.388 |
| R2 | 0.613375 |
| MAPE | 0.196031 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 3_Default_CatBoost
CatBoost
- n_jobs: -1
- learning_rate: 0.1
- depth: 6
- rsm: 1
- loss_function: RMSE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
1.7 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.054 |
| MSE | 28892.7 |
| RMSE | 169.979 |
| R2 | 0.624106 |
| MAPE | 0.19349 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 20_NeuralNetwork
Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 16
- learning_rate: 0.01
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
4.1 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.387 |
| MSE | 29037.8 |
| RMSE | 170.405 |
| R2 | 0.622218 |
| MAPE | 0.193753 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 2_Default_Xgboost
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: reg:squarederror
- eta: 0.075
- max_depth: 6
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
1.8 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.51 |
| MSE | 29059.5 |
| RMSE | 170.468 |
| R2 | 0.621936 |
| MAPE | 0.194258 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 14_CatBoost
CatBoost
- n_jobs: -1
- learning_rate: 0.05
- depth: 8
- rsm: 0.8
- loss_function: RMSE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
2.4 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.021 |
| MSE | 28890.9 |
| RMSE | 169.973 |
| R2 | 0.62413 |
| MAPE | 0.193452 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 15_CatBoost
CatBoost
- n_jobs: -1
- learning_rate: 0.05
- depth: 8
- rsm: 0.7
- loss_function: RMSE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
9.4 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.098 |
| MSE | 28905 |
| RMSE | 170.015 |
| R2 | 0.623946 |
| MAPE | 0.193472 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 6_Xgboost
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: reg:squarederror
- eta: 0.075
- max_depth: 8
- min_child_weight: 5
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
1.9 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 133.423 |
| MSE | 29422.3 |
| RMSE | 171.529 |
| R2 | 0.617216 |
| MAPE | 0.195553 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 5_Default_RandomForest
Random Forest
- n_jobs: -1
- criterion: squared_error
- max_features: 0.9
- min_samples_split: 30
- max_depth: 4
- eval_metric_name: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
5.5 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 134.307 |
| MSE | 29686.6 |
| RMSE | 172.298 |
| R2 | 0.613777 |
| MAPE | 0.195516 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 1_Default_LightGBM
LightGBM
- n_jobs: -1
- objective: regression
- num_leaves: 63
- learning_rate: 0.05
- feature_fraction: 0.9
- bagging_fraction: 0.9
- min_data_in_leaf: 10
- metric: rmse
- custom_eval_metric_name: None
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
5.1 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.86 |
| MSE | 29205.6 |
| RMSE | 170.896 |
| R2 | 0.620035 |
| MAPE | 0.194671 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
